{
  "meta": {
    "pretty_name": "SWE-bench_Lite_Agentic",
    "dataset_id": "princeton-nlp/SWE-bench_Lite",
    "paper_url": null,
    "tags": [
      "Coding"
    ],
    "metrics": [
      "acc"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "default"
    ],
    "description": "\n## Overview\n\nSWE-bench Lite Agentic is the agentic-mode evaluation of SWE-bench Lite, a focused subset of SWE-bench containing 300 Issue-Pull Request pairs from 11 popular Python repositories. The model autonomously drives a multi-turn agent loop inside a per-instance Docker container to resolve real-world GitHub issues.\n\n## Task Description\n\n- **Task Type**: Automated Software Engineering / Bug Fixing (Agentic)\n- **Input**: GitHub issue description (no oracle file context)\n- **Output**: Code patch (diff format) collected from `git diff` after autonomous editing\n- **Size**: 300 carefully selected test instances\n\n## Key Features\n\n- 300 test Issue-Pull Request pairs\n- 11 popular Python repositories covered\n- Real-world bugs with verified solutions\n- Multi-turn agent loop with per-instance Docker sandbox\n- More manageable than full SWE-bench while still challenging\n\n## Evaluation Notes\n\n- Requires `pip install swebench==4.1.0` before evaluation\n- Docker images are built/pulled automatically for each repository\n- See the [usage documentation](https://evalscope.readthedocs.io/en/latest/third_party/swe_bench.html) for detailed setup instructions\n- Popular benchmark variant for initial agentic model comparison\n\n## Agentic Mode\n\nThis benchmark drives a multi-turn agent loop (mirrors mini-swe-agent's\n`swebench.yaml`) inside a per-instance SWE-bench Docker container. The\nmodel issues `bash` commands to explore `/testbed`, edit source files,\nand finally submits its `git diff` patch by printing the sentinel\n`COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT` followed by the patch contents.\n\n`extra_params.action_protocol` selects between:\n- `toolcall` (default): OpenAI function-calling protocol with a single\n  `bash` tool. Recommended for any model that supports tool calling.\n- `backticks`: text-based fallback expecting one\n  ` ```mswea_bash_command ``` ` block per turn. For models without\n  function-calling support.\n",
    "prompt_template": "{question}",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {
      "action_protocol": {
        "type": "str",
        "description": "Agent action protocol: \"toolcall\" (mainline OpenAI function-calling, mirrors mini-swe-agent swebench.yaml) or \"backticks\" (textbased mswea_bash_command fallback for models without function-calling support).",
        "value": "toolcall",
        "choices": [
          "toolcall",
          "backticks"
        ]
      },
      "max_steps": {
        "type": "int",
        "description": "Maximum number of agent steps per sample.",
        "value": 250
      },
      "command_timeout": {
        "type": "float",
        "description": "Default per-bash-command timeout in seconds.",
        "value": 60.0
      },
      "build_docker_images": {
        "type": "bool",
        "description": "Build Docker images locally for each sample.",
        "value": true
      },
      "pull_remote_images_if_available": {
        "type": "bool",
        "description": "Attempt to pull existing remote Docker images before building.",
        "value": true
      },
      "force_arch": {
        "type": "str",
        "description": "Optionally force a specific architecture for image build/pull.",
        "value": "",
        "choices": [
          "",
          "arm64",
          "x86_64"
        ]
      },
      "dockerhub_username": {
        "type": "str",
        "description": "DockerHub user/org namespace for remote SWE-bench images.",
        "value": "swebench"
      }
    },
    "sandbox_config": {},
    "category": "agent"
  },
  "statistics": {
    "total_samples": 300,
    "subset_stats": [
      {
        "name": "default",
        "sample_count": 300,
        "prompt_length_mean": 1661.18,
        "prompt_length_min": 230,
        "prompt_length_max": 24770,
        "prompt_length_std": 2447.98,
        "target_length_mean": null
      }
    ],
    "prompt_length": {
      "mean": 1661.18,
      "min": 230,
      "max": 24770,
      "std": 2447.98
    },
    "target_length_mean": null,
    "computed_at": "2026-05-15T15:00:43.772669"
  },
  "sample_example": {
    "data": {
      "input": [
        {
          "id": "c8f45390",
          "content": "Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\nConsider the following model:\r\n\r\n```python\r\nfrom astropy.modeling import models as m\r\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\r\n       [False, False,  True,  True],\r\n       [False, False,  True,  True]])\r\n```\r\nSuddenly the inputs and outputs are no longer separable?\r\n\r\nThis feels like a bug to me, but I might be missing something?\n"
        }
      ],
      "id": 0,
      "group_id": 0,
      "tools": [
        {
          "name": "bash",
          "description": "Execute a bash command inside the sandbox environment. Returns the combined stdout / stderr output of the command.",
          "parameters": {
            "properties": {
              "command": {
                "type": "string",
                "description": "The bash command to execute."
              },
              "timeout": {
                "type": "number",
                "description": "Maximum execution time in seconds (default: 60).",
                "default": 60
              }
            },
            "required": [
              "command"
            ]
          }
        }
      ],
      "metadata": {
        "problem_statement": "Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\nConsider the following model:\r\n\r\n```python\r\nfrom astropy.modeling import models as m\r\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\r\n       [False, False,  True,  True],\r\n       [False, False,  True,  True]])\r\n```\r\nSuddenly the inputs and outputs are no longer separable?\r\n\r\nThis feels like a bug to me, but I might be missing something?\n",
        "instance_id": "astropy__astropy-12907",
        "base_commit": "d16bfe05a744909de4b27f5875fe0d4ed41ce607",
        "patch": "diff --git a/astropy/modeling/separable.py b/astropy/modeling/separable.py\n--- a/astropy/modeling/separable.py\n+++ b/astropy/modeling/separable.py\n@@ -242,7 +242,7 @@ def _cstack(left, right):\n         cright = _coord_matrix(right, 'right', noutp)\n     else:\n         cright = np.zeros((noutp, right.shape[1]))\n-        cright[-right.shape[0]:, -right.shape[1]:] = 1\n+        cright[-right.shape[0]:, -right.shape[1]:] = right\n \n     return np.hstack([cleft, cright])\n \n",
        "PASS_TO_PASS": [
          "astropy/modeling/tests/test_separable.py::test_coord_matrix",
          "astropy/modeling/tests/test_separable.py::test_cdot",
          "astropy/modeling/tests/test_separable.py::test_cstack",
          "astropy/modeling/tests/test_separable.py::test_arith_oper",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model0-result0]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model1-result1]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model2-result2]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model3-result3]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model4-result4]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model5-result5]",
          "... [TRUNCATED 3 more items] ..."
        ],
        "FAIL_TO_PASS": [
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model6-result6]",
          "astropy/modeling/tests/test_separable.py::test_separable[compound_model9-result9]"
        ],
        "test_patch": "diff --git a/astropy/modeling/tests/test_separable.py b/astropy/modeling/tests/test_separable.py\n--- a/astropy/modeling/tests/test_separable.py\n+++ b/astropy/modeling/tests/test_separable.py\n@@ -28,6 +28,13 @@\n p1 = models.Polynomial1D(1, nam ... [TRUNCATED 931 chars] ...          [True,  True,  False, False, False],\n+                        [False, False, True,  False, False],\n+                        [False, False, False, True,  False],\n+                        [False, False, False, False, True]]))),\n }\n \n \n",
        "version": "4.3",
        "repo": "astropy/astropy",
        "environment_setup_commit": "298ccb478e6bf092953bca67a3d29dc6c35f6752",
        "hints_text": "",
        "created_at": "2022-03-03T15:14:54Z",
        "docker_image": "swebench/sweb.eval.arm64.astropy_1776_astropy-12907:latest"
      }
    },
    "subset": "default",
    "truncated": false
  },
  "readme": {
    "en": "# SWE-bench_Lite_Agentic\n\n\n## Overview\n\nSWE-bench Lite Agentic is the agentic-mode evaluation of SWE-bench Lite, a focused subset of SWE-bench containing 300 Issue-Pull Request pairs from 11 popular Python repositories. The model autonomously drives a multi-turn agent loop inside a per-instance Docker container to resolve real-world GitHub issues.\n\n## Task Description\n\n- **Task Type**: Automated Software Engineering / Bug Fixing (Agentic)\n- **Input**: GitHub issue description (no oracle file context)\n- **Output**: Code patch (diff format) collected from `git diff` after autonomous editing\n- **Size**: 300 carefully selected test instances\n\n## Key Features\n\n- 300 test Issue-Pull Request pairs\n- 11 popular Python repositories covered\n- Real-world bugs with verified solutions\n- Multi-turn agent loop with per-instance Docker sandbox\n- More manageable than full SWE-bench while still challenging\n\n## Evaluation Notes\n\n- Requires `pip install swebench==4.1.0` before evaluation\n- Docker images are built/pulled automatically for each repository\n- See the [usage documentation](https://evalscope.readthedocs.io/en/latest/third_party/swe_bench.html) for detailed setup instructions\n- Popular benchmark variant for initial agentic model comparison\n\n## Agentic Mode\n\nThis benchmark drives a multi-turn agent loop (mirrors mini-swe-agent's\n`swebench.yaml`) inside a per-instance SWE-bench Docker container. The\nmodel issues `bash` commands to explore `/testbed`, edit source files,\nand finally submits its `git diff` patch by printing the sentinel\n`COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT` followed by the patch contents.\n\n`extra_params.action_protocol` selects between:\n- `toolcall` (default): OpenAI function-calling protocol with a single\n  `bash` tool. Recommended for any model that supports tool calling.\n- `backticks`: text-based fallback expecting one\n  ` ```mswea_bash_command ``` ` block per turn. For models without\n  function-calling support.\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `swe_bench_lite_agentic` |\n| **Dataset ID** | [princeton-nlp/SWE-bench_Lite](https://modelscope.cn/datasets/princeton-nlp/SWE-bench_Lite/summary) |\n| **Paper** | N/A |\n| **Tags** | `Coding` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 300 |\n| Prompt Length (Mean) | 1661.18 chars |\n| Prompt Length (Min/Max) | 230 / 24770 chars |\n\n## Sample Example\n\n**Subset**: `default`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"c8f45390\",\n      \"content\": \"Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\\nConsider the following model:\\r\\n\\r\\n```python\\r\\nfrom astropy.modeling import models as m\\r\\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\\r\\n       [False, False,  True,  True],\\r\\n       [False, False,  True,  True]])\\r\\n```\\r\\nSuddenly the inputs and outputs are no longer separable?\\r\\n\\r\\nThis feels like a bug to me, but I might be missing something?\\n\"\n    }\n  ],\n  \"id\": 0,\n  \"group_id\": 0,\n  \"tools\": [\n    {\n      \"name\": \"bash\",\n      \"description\": \"Execute a bash command inside the sandbox environment. Returns the combined stdout / stderr output of the command.\",\n      \"parameters\": {\n        \"properties\": {\n          \"command\": {\n            \"type\": \"string\",\n            \"description\": \"The bash command to execute.\"\n          },\n          \"timeout\": {\n            \"type\": \"number\",\n            \"description\": \"Maximum execution time in seconds (default: 60).\",\n            \"default\": 60\n          }\n        },\n        \"required\": [\n          \"command\"\n        ]\n      }\n    }\n  ],\n  \"metadata\": {\n    \"problem_statement\": \"Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\\nConsider the following model:\\r\\n\\r\\n```python\\r\\nfrom astropy.modeling import models as m\\r\\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\\r\\n       [False, False,  True,  True],\\r\\n       [False, False,  True,  True]])\\r\\n```\\r\\nSuddenly the inputs and outputs are no longer separable?\\r\\n\\r\\nThis feels like a bug to me, but I might be missing something?\\n\",\n    \"instance_id\": \"astropy__astropy-12907\",\n    \"base_commit\": \"d16bfe05a744909de4b27f5875fe0d4ed41ce607\",\n    \"patch\": \"diff --git a/astropy/modeling/separable.py b/astropy/modeling/separable.py\\n--- a/astropy/modeling/separable.py\\n+++ b/astropy/modeling/separable.py\\n@@ -242,7 +242,7 @@ def _cstack(left, right):\\n         cright = _coord_matrix(right, 'right', noutp)\\n     else:\\n         cright = np.zeros((noutp, right.shape[1]))\\n-        cright[-right.shape[0]:, -right.shape[1]:] = 1\\n+        cright[-right.shape[0]:, -right.shape[1]:] = right\\n \\n     return np.hstack([cleft, cright])\\n \\n\",\n    \"PASS_TO_PASS\": [\n      \"astropy/modeling/tests/test_separable.py::test_coord_matrix\",\n      \"astropy/modeling/tests/test_separable.py::test_cdot\",\n      \"astropy/modeling/tests/test_separable.py::test_cstack\",\n      \"astropy/modeling/tests/test_separable.py::test_arith_oper\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model0-result0]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model1-result1]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model2-result2]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model3-result3]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model4-result4]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model5-result5]\",\n      \"... [TRUNCATED 3 more items] ...\"\n    ],\n    \"FAIL_TO_PASS\": [\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model6-result6]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model9-result9]\"\n    ],\n    \"test_patch\": \"diff --git a/astropy/modeling/tests/test_separable.py b/astropy/modeling/tests/test_separable.py\\n--- a/astropy/modeling/tests/test_separable.py\\n+++ b/astropy/modeling/tests/test_separable.py\\n@@ -28,6 +28,13 @@\\n p1 = models.Polynomial1D(1, nam ... [TRUNCATED 931 chars] ...          [True,  True,  False, False, False],\\n+                        [False, False, True,  False, False],\\n+                        [False, False, False, True,  False],\\n+                        [False, False, False, False, True]]))),\\n }\\n \\n \\n\",\n    \"version\": \"4.3\",\n    \"repo\": \"astropy/astropy\",\n    \"environment_setup_commit\": \"298ccb478e6bf092953bca67a3d29dc6c35f6752\",\n    \"hints_text\": \"\",\n    \"created_at\": \"2022-03-03T15:14:54Z\",\n    \"docker_image\": \"swebench/sweb.eval.arm64.astropy_1776_astropy-12907:latest\"\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\n{question}\n```\n\n## Extra Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `action_protocol` | `str` | `toolcall` | Agent action protocol: \"toolcall\" (mainline OpenAI function-calling, mirrors mini-swe-agent swebench.yaml) or \"backticks\" (textbased mswea_bash_command fallback for models without function-calling support). Choices: ['toolcall', 'backticks'] |\n| `max_steps` | `int` | `250` | Maximum number of agent steps per sample. |\n| `command_timeout` | `float` | `60.0` | Default per-bash-command timeout in seconds. |\n| `build_docker_images` | `bool` | `True` | Build Docker images locally for each sample. |\n| `pull_remote_images_if_available` | `bool` | `True` | Attempt to pull existing remote Docker images before building. |\n| `force_arch` | `str` | `` | Optionally force a specific architecture for image build/pull. Choices: ['', 'arm64', 'x86_64'] |\n| `dockerhub_username` | `str` | `swebench` | DockerHub user/org namespace for remote SWE-bench images. |\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets swe_bench_lite_agentic \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['swe_bench_lite_agentic'],\n    dataset_args={\n        'swe_bench_lite_agentic': {\n            # extra_params: {}  # uses default extra parameters\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# SWE-bench_Lite_Agentic\n\n\n## 概述\n\nSWE-bench Lite Agentic 是 SWE-bench Lite 的代理模式（agentic-mode）评估版本。SWE-bench Lite 是 SWE-bench 的一个精选子集，包含来自 11 个热门 Python 仓库的 300 个 Issue-Pull Request 对。模型在每个实例独立的 Docker 容器中自主驱动多轮代理循环，以解决真实的 GitHub 问题。\n\n## 任务描述\n\n- **任务类型**：自动化软件工程 / 缺陷修复（代理模式）\n- **输入**：GitHub issue 描述（不提供 oracle 文件上下文）\n- **输出**：通过 `git diff` 收集的代码补丁（diff 格式），由模型自主编辑后生成\n- **规模**：300 个精心挑选的测试实例\n\n## 主要特性\n\n- 包含 300 个测试用的 Issue-Pull Request 对\n- 覆盖 11 个热门 Python 仓库\n- 真实世界中的缺陷，且已有验证过的解决方案\n- 每个实例使用独立的 Docker 沙箱环境，支持多轮代理循环\n- 相比完整版 SWE-bench 更易于管理，但仍具挑战性\n\n## 评估说明\n\n- 评估前需先执行 `pip install swebench==4.1.0`\n- 每个仓库的 Docker 镜像会自动构建或拉取\n- 详细设置说明请参阅 [使用文档](https://evalscope.readthedocs.io/zh-cn/latest/third_party/swe_bench.html)\n- 此基准测试是初始代理模型对比的常用变体\n\n## 代理模式\n\n该基准测试在每个实例专属的 SWE-bench Docker 容器内驱动一个多轮代理循环（与 mini-swe-agent 的 `swebench.yaml` 配置一致）。模型通过发出 `bash` 命令来探索 `/testbed` 目录、编辑源文件，并最终通过打印哨兵字符串 `COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT` 及其后的补丁内容来提交 `git diff` 补丁。\n\n`extra_params.action_protocol` 可选择以下两种协议：\n- `toolcall`（默认）：采用 OpenAI 函数调用协议，仅提供一个 `bash` 工具。推荐用于支持工具调用的模型。\n- `backticks`：基于文本的备用方案，每轮期望一个 ` ```mswea_bash_command ``` ` 代码块。适用于不支持函数调用的模型。\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `swe_bench_lite_agentic` |\n| **数据集 ID** | [princeton-nlp/SWE-bench_Lite](https://modelscope.cn/datasets/princeton-nlp/SWE-bench_Lite/summary) |\n| **论文** | N/A |\n| **标签** | `Coding` |\n| **指标** | `acc` |\n| **默认示例数** | 0-shot |\n| **评估划分** | `test` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 300 |\n| 提示词长度（平均） | 1661.18 字符 |\n| 提示词长度（最小/最大） | 230 / 24770 字符 |\n\n## 样例示例\n\n**子集**: `default`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"c8f45390\",\n      \"content\": \"Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\\nConsider the following model:\\r\\n\\r\\n```python\\r\\nfrom astropy.modeling import models as m\\r\\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\\r\\n       [False, False,  True,  True],\\r\\n       [False, False,  True,  True]])\\r\\n```\\r\\nSuddenly the inputs and outputs are no longer separable?\\r\\n\\r\\nThis feels like a bug to me, but I might be missing something?\\n\"\n    }\n  ],\n  \"id\": 0,\n  \"group_id\": 0,\n  \"tools\": [\n    {\n      \"name\": \"bash\",\n      \"description\": \"Execute a bash command inside the sandbox environment. Returns the combined stdout / stderr output of the command.\",\n      \"parameters\": {\n        \"properties\": {\n          \"command\": {\n            \"type\": \"string\",\n            \"description\": \"The bash command to execute.\"\n          },\n          \"timeout\": {\n            \"type\": \"number\",\n            \"description\": \"Maximum execution time in seconds (default: 60).\",\n            \"default\": 60\n          }\n        },\n        \"required\": [\n          \"command\"\n        ]\n      }\n    }\n  ],\n  \"metadata\": {\n    \"problem_statement\": \"Modeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\\nConsider the following model:\\r\\n\\r\\n```python\\r\\nfrom astropy.modeling import models as m\\r\\nfrom astropy.modeling.separable import separability_matri ... [TRUNCATED 762 chars] ...       [ True,  True, False, False],\\r\\n       [False, False,  True,  True],\\r\\n       [False, False,  True,  True]])\\r\\n```\\r\\nSuddenly the inputs and outputs are no longer separable?\\r\\n\\r\\nThis feels like a bug to me, but I might be missing something?\\n\",\n    \"instance_id\": \"astropy__astropy-12907\",\n    \"base_commit\": \"d16bfe05a744909de4b27f5875fe0d4ed41ce607\",\n    \"patch\": \"diff --git a/astropy/modeling/separable.py b/astropy/modeling/separable.py\\n--- a/astropy/modeling/separable.py\\n+++ b/astropy/modeling/separable.py\\n@@ -242,7 +242,7 @@ def _cstack(left, right):\\n         cright = _coord_matrix(right, 'right', noutp)\\n     else:\\n         cright = np.zeros((noutp, right.shape[1]))\\n-        cright[-right.shape[0]:, -right.shape[1]:] = 1\\n+        cright[-right.shape[0]:, -right.shape[1]:] = right\\n \\n     return np.hstack([cleft, cright])\\n \\n\",\n    \"PASS_TO_PASS\": [\n      \"astropy/modeling/tests/test_separable.py::test_coord_matrix\",\n      \"astropy/modeling/tests/test_separable.py::test_cdot\",\n      \"astropy/modeling/tests/test_separable.py::test_cstack\",\n      \"astropy/modeling/tests/test_separable.py::test_arith_oper\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model0-result0]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model1-result1]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model2-result2]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model3-result3]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model4-result4]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model5-result5]\",\n      \"... [TRUNCATED 3 more items] ...\"\n    ],\n    \"FAIL_TO_PASS\": [\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model6-result6]\",\n      \"astropy/modeling/tests/test_separable.py::test_separable[compound_model9-result9]\"\n    ],\n    \"test_patch\": \"diff --git a/astropy/modeling/tests/test_separable.py b/astropy/modeling/tests/test_separable.py\\n--- a/astropy/modeling/tests/test_separable.py\\n+++ b/astropy/modeling/tests/test_separable.py\\n@@ -28,6 +28,13 @@\\n p1 = models.Polynomial1D(1, nam ... [TRUNCATED 931 chars] ...          [True,  True,  False, False, False],\\n+                        [False, False, True,  False, False],\\n+                        [False, False, False, True,  False],\\n+                        [False, False, False, False, True]]))),\\n }\\n \\n \\n\",\n    \"version\": \"4.3\",\n    \"repo\": \"astropy/astropy\",\n    \"environment_setup_commit\": \"298ccb478e6bf092953bca67a3d29dc6c35f6752\",\n    \"hints_text\": \"\",\n    \"created_at\": \"2022-03-03T15:14:54Z\",\n    \"docker_image\": \"swebench/sweb.eval.arm64.astropy_1776_astropy-12907:latest\"\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\n{question}\n```\n\n## 额外参数\n\n| 参数 | 类型 | 默认值 | 描述 |\n|-----------|------|---------|-------------|\n| `action_protocol` | `str` | `toolcall` | 代理动作协议：\"toolcall\"（主流 OpenAI 函数调用方式，与 mini-swe-agent 的 swebench.yaml 一致）或 \"backticks\"（基于文本的 mswea_bash_command 回退方案，适用于不支持函数调用的模型）。可选项：['toolcall', 'backticks'] |\n| `max_steps` | `int` | `250` | 每个样本的最大代理步数。 |\n| `command_timeout` | `float` | `60.0` | 每个 bash 命令的默认超时时间（秒）。 |\n| `build_docker_images` | `bool` | `True` | 为每个样本本地构建 Docker 镜像。 |\n| `pull_remote_images_if_available` | `bool` | `True` | 在构建前尝试拉取已存在的远程 Docker 镜像。 |\n| `force_arch` | `str` | `` | 可选地强制指定镜像构建/拉取的目标架构。可选项：['', 'arm64', 'x86_64'] |\n| `dockerhub_username` | `str` | `swebench` | 远程 SWE-bench 镜像所用的 DockerHub 用户/组织命名空间。 |\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets swe_bench_lite_agentic \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['swe_bench_lite_agentic'],\n    dataset_args={\n        'swe_bench_lite_agentic': {\n            # extra_params: {}  # 使用默认额外参数\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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